Overview

Dataset statistics

Number of variables15
Number of observations33819106
Missing cells2078068
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.9 GiB
Average record size in memory376.7 B

Variable types

Numeric10
Categorical3
Text2

Alerts

eval_set is highly imbalanced (75.3%)Imbalance
days_since_prior_order has 2078068 (6.1%) missing valuesMissing
order_dow has 6533692 (19.3%) zerosZeros
days_since_prior_order has 465742 (1.4%) zerosZeros

Reproduction

Analysis started2024-02-14 22:49:43.382485
Analysis finished2024-02-15 01:06:42.936035
Duration2 hours, 16 minutes and 59.55 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

order_id
Real number (ℝ)

Distinct3346083
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1710566.3
Minimum1
Maximum3421083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size516.0 MiB
2024-02-14T20:06:43.438940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile170921
Q1855413
median1710660
Q32565587
95-th percentile3250188
Maximum3421083
Range3421082
Interquartile range (IQR)1710174

Descriptive statistics

Standard deviation987400.76
Coefficient of variation (CV)0.57723619
Kurtosis-1.1994394
Mean1710566.3
Median Absolute Deviation (MAD)855085
Skewness-0.00021110288
Sum5.7849823 × 1013
Variance9.7496026 × 1011
MonotonicityNot monotonic
2024-02-14T20:06:43.759209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1564244 145
 
< 0.1%
790903 137
 
< 0.1%
61355 127
 
< 0.1%
2970392 121
 
< 0.1%
2069920 116
 
< 0.1%
3308010 115
 
< 0.1%
2753324 114
 
< 0.1%
2499774 112
 
< 0.1%
77151 109
 
< 0.1%
2621625 109
 
< 0.1%
Other values (3346073) 33817901
> 99.9%
ValueCountFrequency (%)
1 8
 
< 0.1%
2 9
 
< 0.1%
3 8
 
< 0.1%
4 13
< 0.1%
5 26
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 15
< 0.1%
10 15
< 0.1%
ValueCountFrequency (%)
3421083 10
< 0.1%
3421082 7
< 0.1%
3421081 7
< 0.1%
3421080 9
< 0.1%
3421079 1
 
< 0.1%
3421078 9
< 0.1%
3421077 4
 
< 0.1%
3421076 8
< 0.1%
3421075 8
< 0.1%
3421074 4
 
< 0.1%

product_id
Real number (ℝ)

Distinct49685
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25575.515
Minimum1
Maximum49688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size516.0 MiB
2024-02-14T20:06:44.089391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3376
Q113519
median25256
Q337935
95-th percentile47565
Maximum49688
Range49687
Interquartile range (IQR)24416

Descriptive statistics

Standard deviation14097.697
Coefficient of variation (CV)0.5512185
Kurtosis-1.14135
Mean25575.515
Median Absolute Deviation (MAD)12080
Skewness-0.021181543
Sum8.6494104 × 1011
Variance1.9874505 × 108
MonotonicityNot monotonic
2024-02-14T20:06:44.389848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24852 491291
 
1.5%
13176 394930
 
1.2%
21137 275577
 
0.8%
21903 251705
 
0.7%
47209 220877
 
0.7%
47766 184224
 
0.5%
47626 160792
 
0.5%
16797 149445
 
0.4%
26209 146660
 
0.4%
27845 142813
 
0.4%
Other values (49675) 31400792
92.8%
ValueCountFrequency (%)
1 1928
< 0.1%
2 94
 
< 0.1%
3 283
 
< 0.1%
4 351
 
< 0.1%
5 16
 
< 0.1%
6 8
 
< 0.1%
7 31
 
< 0.1%
8 178
 
< 0.1%
9 161
 
< 0.1%
10 2691
< 0.1%
ValueCountFrequency (%)
49688 93
 
< 0.1%
49687 14
 
< 0.1%
49686 127
 
< 0.1%
49685 49
 
< 0.1%
49684 9
 
< 0.1%
49683 99728
0.3%
49682 113
 
< 0.1%
49681 78
 
< 0.1%
49680 1064
 
< 0.1%
49679 136
 
< 0.1%

add_to_cart_order
Real number (ℝ)

Distinct145
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3677376
Minimum1
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size516.0 MiB
2024-02-14T20:06:44.690471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q311
95-th percentile22
Maximum145
Range144
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.1395401
Coefficient of variation (CV)0.85322228
Kurtosis5.5752723
Mean8.3677376
Median Absolute Deviation (MAD)4
Skewness1.8123897
Sum2.829894 × 108
Variance50.973033
MonotonicityNot monotonic
2024-02-14T20:06:45.110376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3346083
 
9.9%
2 3182490
 
9.4%
3 2988129
 
8.8%
4 2773069
 
8.2%
5 2542770
 
7.5%
6 2305545
 
6.8%
7 2069162
 
6.1%
8 1840615
 
5.4%
9 1629258
 
4.8%
10 1437694
 
4.3%
Other values (135) 9704291
28.7%
ValueCountFrequency (%)
1 3346083
9.9%
2 3182490
9.4%
3 2988129
8.8%
4 2773069
8.2%
5 2542770
7.5%
6 2305545
6.8%
7 2069162
6.1%
8 1840615
5.4%
9 1629258
4.8%
10 1437694
4.3%
ValueCountFrequency (%)
145 1
< 0.1%
144 1
< 0.1%
143 1
< 0.1%
142 1
< 0.1%
141 1
< 0.1%
140 1
< 0.1%
139 1
< 0.1%
138 1
< 0.1%
137 2
< 0.1%
136 2
< 0.1%

reordered
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 GiB
1
19955360 
0
13863746 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33819106
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 19955360
59.0%
0 13863746
41.0%

Length

2024-02-14T20:06:45.353881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-14T20:06:45.614764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 19955360
59.0%
0 13863746
41.0%

Most occurring characters

ValueCountFrequency (%)
1 19955360
59.0%
0 13863746
41.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33819106
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 19955360
59.0%
0 13863746
41.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33819106
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 19955360
59.0%
0 13863746
41.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33819106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19955360
59.0%
0 13863746
41.0%
Distinct49685
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.8 GiB
2024-02-14T20:06:46.335268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length159
Median length124
Mean length25.039137
Min length3

Characters and Unicode

Total characters846801236
Distinct characters112
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)< 0.1%

Sample

1st rowOrganic Egg Whites
2nd rowOrganic Egg Whites
3rd rowOrganic Egg Whites
4th rowOrganic Egg Whites
5th rowOrganic Egg Whites
ValueCountFrequency (%)
organic 10647762
 
8.3%
2102315
 
1.6%
milk 1815686
 
1.4%
cheese 1526255
 
1.2%
yogurt 1408187
 
1.1%
whole 1278905
 
1.0%
free 1203249
 
0.9%
original 1039155
 
0.8%
water 1034453
 
0.8%
baby 1033852
 
0.8%
Other values (12050) 105843821
82.1%
2024-02-14T20:06:47.452351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
95298771
 
11.3%
e 79500100
 
9.4%
a 72043893
 
8.5%
r 59745631
 
7.1%
i 49944474
 
5.9%
n 46247525
 
5.5%
o 37243085
 
4.4%
l 35052759
 
4.1%
t 34937731
 
4.1%
s 28299573
 
3.3%
Other values (102) 308487694
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 615403153
72.7%
Uppercase Letter 125012253
 
14.8%
Space Separator 95305265
 
11.3%
Other Punctuation 5593466
 
0.7%
Decimal Number 4009771
 
0.5%
Dash Punctuation 994372
 
0.1%
Close Punctuation 161110
 
< 0.1%
Open Punctuation 161110
 
< 0.1%
Math Symbol 89218
 
< 0.1%
Other Symbol 70603
 
< 0.1%
Other values (4) 915
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 79500100
12.9%
a 72043893
11.7%
r 59745631
9.7%
i 49944474
 
8.1%
n 46247525
 
7.5%
o 37243085
 
6.1%
l 35052759
 
5.7%
t 34937731
 
5.7%
s 28299573
 
4.6%
c 27358183
 
4.4%
Other values (29) 145030199
23.6%
Uppercase Letter
ValueCountFrequency (%)
C 15335757
12.3%
S 14160214
11.3%
O 14159001
11.3%
B 12856452
 
10.3%
P 7925411
 
6.3%
F 6229105
 
5.0%
M 6012593
 
4.8%
G 5840883
 
4.7%
A 5110267
 
4.1%
R 4898369
 
3.9%
Other values (16) 32484201
26.0%
Other Punctuation
ValueCountFrequency (%)
& 1964483
35.1%
, 1475039
26.4%
% 1328625
23.8%
' 317239
 
5.7%
/ 203451
 
3.6%
! 101512
 
1.8%
. 99198
 
1.8%
: 38189
 
0.7%
\ 26695
 
0.5%
" 26695
 
0.5%
Other values (5) 12340
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 1393534
34.8%
1 902835
22.5%
2 760298
19.0%
3 269733
 
6.7%
5 164604
 
4.1%
4 162011
 
4.0%
9 121074
 
3.0%
8 90551
 
2.3%
7 73228
 
1.8%
6 71903
 
1.8%
Other Symbol
ValueCountFrequency (%)
® 43448
61.5%
26961
38.2%
144
 
0.2%
° 50
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 990913
99.7%
3223
 
0.3%
236
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 152
87.4%
˚ 12
 
6.9%
` 10
 
5.7%
Space Separator
ValueCountFrequency (%)
95298771
> 99.9%
  6494
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 161060
> 99.9%
} 50
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 161060
> 99.9%
{ 50
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 84567
94.8%
= 4651
 
5.2%
Final Punctuation
ValueCountFrequency (%)
154
94.5%
9
 
5.5%
Currency Symbol
ValueCountFrequency (%)
$ 360
100.0%
Control
ValueCountFrequency (%)
 218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 740413517
87.4%
Common 106385830
 
12.6%
Cyrillic 1889
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 79500100
 
10.7%
a 72043893
 
9.7%
r 59745631
 
8.1%
i 49944474
 
6.7%
n 46247525
 
6.2%
o 37243085
 
5.0%
l 35052759
 
4.7%
t 34937731
 
4.7%
s 28299573
 
3.8%
c 27358183
 
3.7%
Other values (54) 270040563
36.5%
Common
ValueCountFrequency (%)
95298771
89.6%
& 1964483
 
1.8%
, 1475039
 
1.4%
0 1393534
 
1.3%
% 1328625
 
1.2%
- 990913
 
0.9%
1 902835
 
0.8%
2 760298
 
0.7%
' 317239
 
0.3%
3 269733
 
0.3%
Other values (37) 1684360
 
1.6%
Cyrillic
ValueCountFrequency (%)
е 1889
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 846698862
> 99.9%
None 69746
 
< 0.1%
Letterlike Symbols 26961
 
< 0.1%
Punctuation 3622
 
< 0.1%
Cyrillic 1889
 
< 0.1%
Specials 144
 
< 0.1%
Modifier Letters 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
95298771
 
11.3%
e 79500100
 
9.4%
a 72043893
 
8.5%
r 59745631
 
7.1%
i 49944474
 
5.9%
n 46247525
 
5.5%
o 37243085
 
4.4%
l 35052759
 
4.1%
t 34937731
 
4.1%
s 28299573
 
3.3%
Other values (77) 308385320
36.4%
None
ValueCountFrequency (%)
® 43448
62.3%
é 11850
 
17.0%
  6494
 
9.3%
ñ 4043
 
5.8%
è 2703
 
3.9%
í 469
 
0.7%
 218
 
0.3%
´ 152
 
0.2%
â 115
 
0.2%
ç 68
 
0.1%
Other values (7) 186
 
0.3%
Letterlike Symbols
ValueCountFrequency (%)
26961
100.0%
Punctuation
ValueCountFrequency (%)
3223
89.0%
236
 
6.5%
154
 
4.3%
9
 
0.2%
Cyrillic
ValueCountFrequency (%)
е 1889
100.0%
Specials
ValueCountFrequency (%)
144
100.0%
Modifier Letters
ValueCountFrequency (%)
˚ 12
100.0%

aisle_id
Real number (ℝ)

Distinct134
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.217985
Minimum1
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size516.0 MiB
2024-02-14T20:06:47.655736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q131
median83
Q3107
95-th percentile123
Maximum134
Range133
Interquartile range (IQR)76

Descriptive statistics

Standard deviation38.198982
Coefficient of variation (CV)0.53636706
Kurtosis-1.3243685
Mean71.217985
Median Absolute Deviation (MAD)33
Skewness-0.16791174
Sum2.4085286 × 109
Variance1459.1622
MonotonicityNot monotonic
2024-02-14T20:06:49.154703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 3792661
 
11.2%
83 3568630
 
10.6%
123 1843806
 
5.5%
120 1507583
 
4.5%
21 1021462
 
3.0%
84 923659
 
2.7%
115 878150
 
2.6%
107 753739
 
2.2%
91 664493
 
2.0%
112 608469
 
1.8%
Other values (124) 18256454
54.0%
ValueCountFrequency (%)
1 74864
 
0.2%
2 86364
 
0.3%
3 473835
1.4%
4 210604
0.6%
5 65415
 
0.2%
6 38086
 
0.1%
7 35391
 
0.1%
8 36372
 
0.1%
9 228123
0.7%
10 9620
 
< 0.1%
ValueCountFrequency (%)
134 11659
 
< 0.1%
133 19580
 
0.1%
132 6455
 
< 0.1%
131 277935
0.8%
130 164516
0.5%
129 203452
0.6%
128 201650
0.6%
127 42905
 
0.1%
126 20801
 
0.1%
125 37053
 
0.1%

department_id
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9185436
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size516.0 MiB
2024-02-14T20:06:49.420398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q316
95-th percentile19
Maximum21
Range20
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.281655
Coefficient of variation (CV)0.63332433
Kurtosis-1.5598625
Mean9.9185436
Median Absolute Deviation (MAD)5
Skewness0.15202009
Sum3.3543628 × 108
Variance39.459189
MonotonicityNot monotonic
2024-02-14T20:06:49.747589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 9888378
29.2%
16 5631067
16.7%
19 3006412
 
8.9%
7 2804175
 
8.3%
1 2336858
 
6.9%
13 1956819
 
5.8%
3 1225181
 
3.6%
15 1114857
 
3.3%
20 1095540
 
3.2%
9 905340
 
2.7%
Other values (11) 3854479
 
11.4%
ValueCountFrequency (%)
1 2336858
 
6.9%
2 38086
 
0.1%
3 1225181
 
3.6%
4 9888378
29.2%
5 159294
 
0.5%
6 281155
 
0.8%
7 2804175
 
8.3%
8 102221
 
0.3%
9 905340
 
2.7%
10 35932
 
0.1%
ValueCountFrequency (%)
21 77396
 
0.2%
20 1095540
 
3.2%
19 3006412
8.9%
18 438743
 
1.3%
17 774652
 
2.3%
16 5631067
16.7%
15 1114857
 
3.3%
14 739069
 
2.2%
13 1956819
 
5.8%
12 739238
 
2.2%

user_id
Real number (ℝ)

Distinct206209
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102944.43
Minimum1
Maximum206209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size516.0 MiB
2024-02-14T20:06:50.307568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10468
Q151435
median102626
Q3154412
95-th percentile195732
Maximum206209
Range206208
Interquartile range (IQR)102977

Descriptive statistics

Standard deviation59467.334
Coefficient of variation (CV)0.57766444
Kurtosis-1.2009175
Mean102944.43
Median Absolute Deviation (MAD)51505
Skewness0.0063277961
Sum3.4814885 × 1012
Variance3.5363638 × 109
MonotonicityNot monotonic
2024-02-14T20:06:50.734638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201268 3725
 
< 0.1%
129928 3689
 
< 0.1%
164055 3089
 
< 0.1%
176478 2952
 
< 0.1%
186704 2936
 
< 0.1%
137629 2931
 
< 0.1%
182401 2929
 
< 0.1%
33731 2912
 
< 0.1%
108187 2760
 
< 0.1%
4694 2735
 
< 0.1%
Other values (206199) 33788448
99.9%
ValueCountFrequency (%)
1 70
 
< 0.1%
2 226
< 0.1%
3 88
 
< 0.1%
4 18
 
< 0.1%
5 46
 
< 0.1%
6 14
 
< 0.1%
7 215
< 0.1%
8 67
 
< 0.1%
9 98
< 0.1%
10 147
< 0.1%
ValueCountFrequency (%)
206209 137
 
< 0.1%
206208 677
< 0.1%
206207 223
 
< 0.1%
206206 285
< 0.1%
206205 51
 
< 0.1%
206204 54
 
< 0.1%
206203 132
 
< 0.1%
206202 198
 
< 0.1%
206201 404
< 0.1%
206200 298
< 0.1%

eval_set
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 GiB
prior
32434489 
train
 
1384617

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters169095530
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowprior
2nd rowprior
3rd rowprior
4th rowprior
5th rowprior

Common Values

ValueCountFrequency (%)
prior 32434489
95.9%
train 1384617
 
4.1%

Length

2024-02-14T20:06:51.105400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-14T20:06:51.317729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
prior 32434489
95.9%
train 1384617
 
4.1%

Most occurring characters

ValueCountFrequency (%)
r 66253595
39.2%
i 33819106
20.0%
p 32434489
19.2%
o 32434489
19.2%
t 1384617
 
0.8%
a 1384617
 
0.8%
n 1384617
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 169095530
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 66253595
39.2%
i 33819106
20.0%
p 32434489
19.2%
o 32434489
19.2%
t 1384617
 
0.8%
a 1384617
 
0.8%
n 1384617
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 169095530
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 66253595
39.2%
i 33819106
20.0%
p 32434489
19.2%
o 32434489
19.2%
t 1384617
 
0.8%
a 1384617
 
0.8%
n 1384617
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169095530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 66253595
39.2%
i 33819106
20.0%
p 32434489
19.2%
o 32434489
19.2%
t 1384617
 
0.8%
a 1384617
 
0.8%
n 1384617
 
0.8%

order_number
Real number (ℝ)

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.139977
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size516.0 MiB
2024-02-14T20:06:51.601075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q324
95-th percentile54
Maximum100
Range99
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.498287
Coefficient of variation (CV)1.0209049
Kurtosis3.3469678
Mean17.139977
Median Absolute Deviation (MAD)8
Skewness1.7742556
Sum5.796587 × 108
Variance306.19006
MonotonicityNot monotonic
2024-02-14T20:06:51.836651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2078068
 
6.1%
3 2050731
 
6.1%
2 2048332
 
6.1%
4 1970180
 
5.8%
5 1751959
 
5.2%
6 1577566
 
4.7%
7 1424796
 
4.2%
8 1294793
 
3.8%
9 1188834
 
3.5%
10 1088920
 
3.2%
Other values (90) 17344927
51.3%
ValueCountFrequency (%)
1 2078068
6.1%
2 2048332
6.1%
3 2050731
6.1%
4 1970180
5.8%
5 1751959
5.2%
6 1577566
4.7%
7 1424796
4.2%
8 1294793
3.8%
9 1188834
3.5%
10 1088920
3.2%
ValueCountFrequency (%)
100 7624
< 0.1%
99 12686
< 0.1%
98 13150
< 0.1%
97 13691
< 0.1%
96 14215
< 0.1%
95 14901
< 0.1%
94 15643
< 0.1%
93 15765
< 0.1%
92 16645
< 0.1%
91 17232
0.1%

order_dow
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7372854
Minimum0
Maximum6
Zeros6533692
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size516.0 MiB
2024-02-14T20:06:52.073786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0932956
Coefficient of variation (CV)0.76473414
Kurtosis-1.3366802
Mean2.7372854
Median Absolute Deviation (MAD)2
Skewness0.17983282
Sum92572545
Variance4.3818865
MonotonicityNot monotonic
2024-02-14T20:06:52.203477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 6533692
19.3%
1 5871834
17.4%
6 4707583
13.9%
5 4386443
13.0%
2 4378360
12.9%
3 3998498
11.8%
4 3942696
11.7%
ValueCountFrequency (%)
0 6533692
19.3%
1 5871834
17.4%
2 4378360
12.9%
3 3998498
11.8%
4 3942696
11.7%
5 4386443
13.0%
6 4707583
13.9%
ValueCountFrequency (%)
6 4707583
13.9%
5 4386443
13.0%
4 3942696
11.7%
3 3998498
11.8%
2 4378360
12.9%
1 5871834
17.4%
0 6533692
19.3%

order_hour_of_day
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.431226
Minimum0
Maximum23
Zeros228031
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size516.0 MiB
2024-02-14T20:06:52.521919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median13
Q316
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2461492
Coefficient of variation (CV)0.31614011
Kurtosis-0.009830582
Mean13.431226
Median Absolute Deviation (MAD)3
Skewness-0.047226772
Sum4.5423205 × 108
Variance18.029783
MonotonicityNot monotonic
2024-02-14T20:06:52.722195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 2874905
 
8.5%
11 2852701
 
8.4%
14 2810918
 
8.3%
15 2780731
 
8.2%
13 2778054
 
8.2%
12 2732599
 
8.1%
16 2647695
 
7.8%
9 2550569
 
7.5%
17 2186409
 
6.5%
8 1787359
 
5.3%
Other values (14) 7817166
23.1%
ValueCountFrequency (%)
0 228031
 
0.7%
1 121412
 
0.4%
2 72660
 
0.2%
3 53759
 
0.2%
4 55714
 
0.2%
5 91909
 
0.3%
6 302642
 
0.9%
7 928239
 
2.7%
8 1787359
5.3%
9 2550569
7.5%
ValueCountFrequency (%)
23 419585
 
1.2%
22 662053
 
2.0%
21 831183
 
2.5%
20 1017958
 
3.0%
19 1317576
3.9%
18 1714445
5.1%
17 2186409
6.5%
16 2647695
7.8%
15 2780731
8.2%
14 2810918
8.3%

days_since_prior_order
Real number (ℝ)

MISSING  ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing2078068
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean11.364153
Minimum0
Maximum30
Zeros465742
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size516.0 MiB
2024-02-14T20:06:52.917910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q315
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.9405
Coefficient of variation (CV)0.78672826
Kurtosis-0.21991042
Mean11.364153
Median Absolute Deviation (MAD)4
Skewness1.0038359
Sum3.6071 × 108
Variance79.93254
MonotonicityNot monotonic
2024-02-14T20:06:53.189135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7 3586305
 
10.6%
30 3477322
 
10.3%
6 2592077
 
7.7%
5 2180537
 
6.4%
4 2126287
 
6.3%
8 1995636
 
5.9%
3 1914431
 
5.7%
2 1492379
 
4.4%
9 1262383
 
3.7%
14 1082295
 
3.2%
Other values (21) 10031386
29.7%
(Missing) 2078068
 
6.1%
ValueCountFrequency (%)
0 465742
 
1.4%
1 960381
 
2.8%
2 1492379
4.4%
3 1914431
5.7%
4 2126287
6.3%
5 2180537
6.4%
6 2592077
7.7%
7 3586305
10.6%
8 1995636
5.9%
9 1262383
 
3.7%
ValueCountFrequency (%)
30 3477322
10.3%
29 191037
 
0.6%
28 274572
 
0.8%
27 219711
 
0.6%
26 190449
 
0.6%
25 194638
 
0.6%
24 207537
 
0.6%
23 241605
 
0.7%
22 329575
 
1.0%
21 473730
 
1.4%

aisle
Text

Distinct134
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 GiB
2024-02-14T20:06:54.003214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length29
Median length23
Mean length14.450213
Min length3

Characters and Unicode

Total characters488693272
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roweggs
2nd roweggs
3rd roweggs
4th roweggs
5th roweggs
ValueCountFrequency (%)
fresh 8164266
 
11.4%
vegetables 5740245
 
8.0%
fruits 5654560
 
7.9%
packaged 3336032
 
4.6%
frozen 1815757
 
2.5%
water 1756300
 
2.4%
yogurt 1507583
 
2.1%
ice 1053908
 
1.5%
cheese 1021462
 
1.4%
milk 923659
 
1.3%
Other values (194) 40904008
56.9%
2024-02-14T20:06:54.986546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 69815725
14.3%
s 46320231
 
9.5%
r 41408867
 
8.5%
a 38341120
 
7.8%
38058674
 
7.8%
t 28715171
 
5.9%
f 21059844
 
4.3%
i 19554782
 
4.0%
o 18449477
 
3.8%
g 18244592
 
3.7%
Other values (16) 148724789
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 450634598
92.2%
Space Separator 38058674
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 69815725
15.5%
s 46320231
 
10.3%
r 41408867
 
9.2%
a 38341120
 
8.5%
t 28715171
 
6.4%
f 21059844
 
4.7%
i 19554782
 
4.3%
o 18449477
 
4.1%
g 18244592
 
4.0%
c 17451700
 
3.9%
Other values (15) 131273089
29.1%
Space Separator
ValueCountFrequency (%)
38058674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 450634598
92.2%
Common 38058674
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 69815725
15.5%
s 46320231
 
10.3%
r 41408867
 
9.2%
a 38341120
 
8.5%
t 28715171
 
6.4%
f 21059844
 
4.7%
i 19554782
 
4.3%
o 18449477
 
4.1%
g 18244592
 
4.0%
c 17451700
 
3.9%
Other values (15) 131273089
29.1%
Common
ValueCountFrequency (%)
38058674
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 488693272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 69815725
14.3%
s 46320231
 
9.5%
r 41408867
 
8.5%
a 38341120
 
7.8%
38058674
 
7.8%
t 28715171
 
5.9%
f 21059844
 
4.3%
i 19554782
 
4.0%
o 18449477
 
3.8%
g 18244592
 
3.7%
Other values (16) 148724789
30.4%

department
Categorical

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 GiB
produce
9888378 
dairy eggs
5631067 
snacks
3006412 
beverages
2804175 
frozen
2336858 
Other values (16)
10152216 

Length

Max length15
Median length13
Mean length7.9994354
Min length4

Characters and Unicode

Total characters270533754
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdairy eggs
2nd rowdairy eggs
3rd rowdairy eggs
4th rowdairy eggs
5th rowdairy eggs

Common Values

ValueCountFrequency (%)
produce 9888378
29.2%
dairy eggs 5631067
16.7%
snacks 3006412
 
8.9%
beverages 2804175
 
8.3%
frozen 2336858
 
6.9%
pantry 1956819
 
5.8%
bakery 1225181
 
3.6%
canned goods 1114857
 
3.3%
deli 1095540
 
3.2%
dry goods pasta 905340
 
2.7%
Other values (11) 3854479
 
11.4%

Length

2024-02-14T20:06:55.337106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
produce 9888378
22.7%
dairy 5631067
12.9%
eggs 5631067
12.9%
snacks 3006412
 
6.9%
beverages 2804175
 
6.4%
frozen 2336858
 
5.4%
goods 2020197
 
4.6%
pantry 1956819
 
4.5%
bakery 1225181
 
2.8%
canned 1114857
 
2.6%
Other values (16) 7968630
18.3%

Most occurring characters

ValueCountFrequency (%)
e 34494194
12.8%
r 26743514
 
9.9%
a 22603538
 
8.4%
d 22169269
 
8.2%
s 20791011
 
7.7%
o 20399932
 
7.5%
g 16163902
 
6.0%
c 14637634
 
5.4%
p 13321451
 
4.9%
n 10919357
 
4.0%
Other values (13) 68289952
25.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260769219
96.4%
Space Separator 9764535
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 34494194
13.2%
r 26743514
10.3%
a 22603538
 
8.7%
d 22169269
 
8.5%
s 20791011
 
8.0%
o 20399932
 
7.8%
g 16163902
 
6.2%
c 14637634
 
5.6%
p 13321451
 
5.1%
n 10919357
 
4.2%
Other values (12) 58525417
22.4%
Space Separator
ValueCountFrequency (%)
9764535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 260769219
96.4%
Common 9764535
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 34494194
13.2%
r 26743514
10.3%
a 22603538
 
8.7%
d 22169269
 
8.5%
s 20791011
 
8.0%
o 20399932
 
7.8%
g 16163902
 
6.2%
c 14637634
 
5.6%
p 13321451
 
5.1%
n 10919357
 
4.2%
Other values (12) 58525417
22.4%
Common
ValueCountFrequency (%)
9764535
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270533754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 34494194
12.8%
r 26743514
 
9.9%
a 22603538
 
8.4%
d 22169269
 
8.2%
s 20791011
 
7.7%
o 20399932
 
7.5%
g 16163902
 
6.0%
c 14637634
 
5.4%
p 13321451
 
4.9%
n 10919357
 
4.0%
Other values (13) 68289952
25.2%

Interactions

2024-02-14T19:57:00.292064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:43:39.085887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:45:18.937429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:47:05.820634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:48:24.372387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:49:36.021315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:50:48.471921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:52:02.891310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:53:29.755195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:55:14.281264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:57:11.594852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:43:49.298513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:45:29.472181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:47:14.035654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:48:31.444145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:49:43.511937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:50:55.757225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:52:10.391509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:53:39.789395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:55:25.102087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:57:22.151842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:43:57.875295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:45:40.344012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:47:22.605183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:48:38.672673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:49:50.123209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:51:03.340777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:52:17.691524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:53:49.730144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:55:35.510203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:57:33.799180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:44:08.703261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:45:51.353987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:47:30.471687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:48:45.539129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:49:57.181731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:51:10.640881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:52:25.231139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:54:00.310928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:55:45.693477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:57:45.214568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:44:17.750881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:46:02.011848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:47:38.175033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:48:52.672660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:50:04.323507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:51:18.224589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:52:32.725230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:54:10.973351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:55:56.295796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:57:56.507863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:44:28.169413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:46:13.173104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:47:46.439698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:49:00.128041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:50:11.639987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:51:25.856851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:52:43.441829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:54:21.632095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:56:06.995760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:58:07.849578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:44:38.300968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:46:24.192788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:47:53.826416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:49:07.269413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:50:18.901684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:51:33.283400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:52:51.014890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:54:32.142715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:56:17.541850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:58:19.195360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:44:48.296697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:46:34.753268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:48:01.823734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:49:14.406049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:50:26.171349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:51:40.457874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:52:59.175699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:54:42.493729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:56:27.915742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:58:30.049281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:44:57.986072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:46:45.498377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:48:09.187998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:49:21.639747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:50:33.257416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:51:48.047306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:53:08.991951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:54:52.963663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:56:38.177885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:58:40.388298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:45:08.903550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:46:56.767773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:48:17.415144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:49:28.974817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:50:41.057438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:51:55.576288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:53:19.588795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:55:03.714681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-14T19:56:49.015704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-14T20:06:55.548958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
add_to_cart_orderaisle_iddays_since_prior_orderdepartmentdepartment_ideval_setorder_doworder_hour_of_dayorder_idorder_numberproduct_idreordereduser_id
add_to_cart_order1.0000.0060.0760.0300.0150.010-0.015-0.015-0.0000.0010.0090.0970.000
aisle_id0.0061.0000.0060.4380.0210.006-0.002-0.002-0.0000.0010.0050.0880.000
days_since_prior_order0.0760.0061.0000.018-0.0010.140-0.043-0.0020.000-0.3830.0010.1410.000
department0.0300.4380.0181.000-0.0990.019-0.0110.002-0.0000.0080.0470.198-0.001
department_id0.0150.021-0.001-0.0991.0000.0070.006-0.012-0.0000.005-0.0220.1580.000
eval_set0.0100.0060.1400.0190.0071.000-0.0050.008-0.0010.022-0.0000.0040.001
order_dow-0.015-0.002-0.043-0.0110.006-0.0051.0000.0120.0010.014-0.0030.017-0.002
order_hour_of_day-0.015-0.002-0.0020.002-0.0120.0080.0121.0000.001-0.0470.0010.0370.000
order_id-0.000-0.0000.000-0.000-0.000-0.0010.0010.0011.000-0.000-0.0000.001-0.001
order_number0.0010.001-0.3830.0080.0050.0220.014-0.047-0.0001.000-0.0020.337-0.001
product_id0.0090.0050.0010.047-0.022-0.000-0.0030.001-0.000-0.0021.0000.0400.000
reordered0.0970.0880.1410.1980.1580.0040.0170.0370.0010.3370.0401.000-0.001
user_id0.0000.0000.000-0.0010.0000.001-0.0020.000-0.001-0.0010.000-0.0011.000

Missing values

2024-02-14T19:59:05.220029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-14T20:00:52.097475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_idproduct_idadd_to_cart_orderreorderedproduct_nameaisle_iddepartment_iduser_ideval_setorder_numberorder_doworder_hour_of_daydays_since_prior_orderaisledepartment
023312011Organic Egg Whites8616202279prior3598.0eggsdairy eggs
1263312050Organic Egg Whites8616153404prior20167.0eggsdairy eggs
212033120130Organic Egg Whites861623750prior116810.0eggsdairy eggs
33273312051Organic Egg Whites861658707prior21698.0eggsdairy eggs
439033120281Organic Egg Whites8616166654prior480129.0eggsdairy eggs
55373312021Organic Egg Whites8616180135prior15283.0eggsdairy eggs
65823312071Organic Egg Whites8616193223prior621910.0eggsdairy eggs
76083312051Organic Egg Whites861691030prior1132112.0eggsdairy eggs
86233312011Organic Egg Whites861637804prior633123.0eggsdairy eggs
96893312041Organic Egg Whites8616108932prior161133.0eggsdairy eggs
order_idproduct_idadd_to_cart_orderreorderedproduct_nameaisle_iddepartment_iduser_ideval_setorder_numberorder_doworder_hour_of_daydays_since_prior_orderaisledepartment
338190962637713221610California Champagne1345161786prior146320.0specialty wines champagnesalcohol
338190973091866221611California Champagne1345161786prior225132.0specialty wines champagnesalcohol
338190983134093221611California Champagne1345161786prior213153.0specialty wines champagnesalcohol
338190998861321282511Sangria1345137204prior136813.0specialty wines champagnesalcohol
3381910018942012599210Cristal Champagne1345168275prior2401418.0specialty wines champagnesalcohol
3381910132431562073110Straight Sherry1345166400prior311312.0specialty wines champagnesalcohol
338191028608623058210Natural Champagne1345104017prior1351418.0specialty wines champagnesalcohol
3381910313334722790610Imperial Champagne134562079prior1031010.0specialty wines champagnesalcohol
3381910421227012608611La Grand Dame Brut Champagne134577799prior23143.0specialty wines champagnesalcohol
3381910531684752608611La Grand Dame Brut Champagne13455223prior73132.0specialty wines champagnesalcohol